Modern vehicles are often fitted with a suite of environment detection sensors that are designed to detect objects and landscape features around the vehicle in real-time that can be used as a foundation for many present and emerging technologies such as lane change assistance, collision avoidance, and autonomous driving capabilities. Some commonly used sensing systems include optical sensors (e.g., infra-red, cameras, etc.), radio detection and ranging (RADAR) for detecting presence, direction, distance, and speeds of other vehicles or objects, magnetometers (e.g., passive sensing of large ferrous objects, such as trucks, cars, or rail cars), and light detection and ranging (LiDAR).
LiDAR typically uses a pulsed light source and detection system to estimate distances to environmental features (e.g., vehicles, structures, etc.). In some systems, a laser or burst of light (pulse) is emitted and focused through a lens assembly and a reflection of the pulse off of an object is collected by a receiver. A time-of-flight (TOF) of the pulse can be measured from the time of emission to the time the reflection is received, which may manifest as a single data point. This process can be repeated very rapidly over any desired range (typically 360 degrees over a 2D plane for ground-based vehicles, and a 3D region for aircraft) to form a collection of points that are dynamically and continuously updated in real-time, forming a “point cloud.” The point cloud data can be used to estimate, for example, a distance, dimension, and location of the object relative to the LiDAR system, often with very high fidelity (e.g., within 5 cm).
Despite the promise that LiDAR and other sensing systems bring to the continued development of fully autonomous transportation, there are challenges that limit its widespread adoption. LiDAR systems are often expensive, large, and bulky. In some cases, multiple emitters may be needed to accurate track a scene, particularly for systems that require accuracy over a large range and field-of-view (FOV). While significant strides have been made to push autonomous vehicle technology to greater commercial adoption, more improvements are needed.